import cv2
import numpy as np
from dataclasses import dataclass, field
from typing import List, Optional, Set
from ..base_processor import BaseProcessor
from ..pipeline_data import PipelineData


@dataclass
class DetectionClass:
    """检测类别数据类"""
    class_id: int  # 类别ID（对应COCO数据集）
    class_name: str  # 类别名称
    color: tuple  # 绘制颜色 (B, G, R)

    def __hash__(self):
        """用于支持集合操作"""
        return hash(self.class_id)

    def __eq__(self, other):
        """用于支持集合操作"""
        if not isinstance(other, DetectionClass):
            return False
        return self.class_id == other.class_id


class DrawObjectBoxProcessor(BaseProcessor):
    """对象框绘制处理器：绘制检测框、ID、标签、置信度"""

    def __init__(self,
                 name: str = "对象框绘制处理器",
                 show_id: bool = True,
                 show_label: bool = True,
                 show_conf: bool = False,  # 默认不显示置信度
                 box_thickness: int = 2,
                 font_scale: float = 0.5,
                 vehicle_classes: Optional[Set[DetectionClass]] = None):
        super().__init__(name)
        self.show_id = show_id
        self.show_label = show_label
        self.show_conf = show_conf
        self.box_thickness = box_thickness
        self.font_scale = font_scale

        # 初始化车辆类别集合（使用预定义或用户自定义）
        self.vehicle_classes: Set[DetectionClass] = vehicle_classes or [
            # coco 数据集 预设类
            DetectionClass(class_id=0, class_name="person", color=(255, 0, 0)),  # 蓝色
            DetectionClass(class_id=1, class_name="bicycle", color=(60, 100, 180)),  # 深蓝
            DetectionClass(class_id=2, class_name="car", color=(180, 160, 40)),  # 深黄
            DetectionClass(class_id=3, class_name="motorcycle", color=(140, 80, 140)),  # 深紫
            DetectionClass(class_id=5, class_name="bus", color=(40, 160, 160)),  # 深青
            DetectionClass(class_id=7, class_name="truck", color=(180, 120, 40)),  # 深橙
            DetectionClass(class_id=8, class_name="boat", color=(60, 120, 60)),  # 深绿
        ]

        # 构建快速查询映射（提高查找效率）
        self._class_id_to_info = {
            cls.class_id: (cls.class_name, cls.color)
            for cls in self.vehicle_classes
        }
        # 车辆类别ID集合（用于快速判断）
        self._vehicle_class_ids = {cls.class_id for cls in self.vehicle_classes}

    def process(self, data: PipelineData) -> PipelineData:
        """绘制检测框和相关信息"""
        if data.frame is None or data.current_result is None:
            return data

        vis = data.frame.copy()
        boxes = getattr(data.current_result, "boxes", None)

        if boxes is None:
            data.frame = vis
            return data

        # 绘制每个检测框
        for box in boxes:
            try:
                self._draw_single_box(vis, box)
            except Exception as e:
                self.logger.warning(f"绘制检测框失败: {e}")
                continue

        data.frame = vis
        return data

    def _draw_single_box(self, vis: np.ndarray, box):
        """绘制单个检测框"""
        # 获取坐标
        xyxy = getattr(box, "xyxy", None)
        if xyxy is None:
            return

        x1, y1, x2, y2 = map(int, map(float, xyxy[0].tolist()))

        # 获取类别和置信度
        cls = int(getattr(box, "cls", -1))
        conf = float(getattr(box, "conf", 0.0))

        # 获取track_id
        track_id = int(getattr(box, "id", -1))

        # 检查是否是需要绘制的车辆类别
        if cls not in self._vehicle_class_ids:
            return

        # 获取类别名称和颜色
        class_name, color = self._class_id_to_info.get(
            cls, ("unknown", (0, 255, 255))  # 默认白色文字，黄色框
        )

        # 绘制检测框
        cv2.rectangle(vis, (x1, y1), (x2, y2), color, self.box_thickness)

        # 构建显示文本
        text_parts = []

        if self.show_id and track_id != -1:
            text_parts.append(f"ID:{track_id}")

        if self.show_label:
            text_parts.append(class_name)

        if self.show_conf:
            text_parts.append(f"{conf:.2f}")

        if text_parts:
            text = " ".join(text_parts)

            # 计算文本大小
            text_size = cv2.getTextSize(
                text, cv2.FONT_HERSHEY_SIMPLEX, self.font_scale, 1
            )[0]

            # 确保文本位置在图像内
            text_y = max(y1 - 10, 20)  # 至少离顶部20像素
            text_x = max(x1, 10)  # 至少离左侧10像素

            # 绘制文本背景（带内边距）
            bg_top_left = (text_x - 2, text_y - text_size[1] - 4)
            bg_bottom_right = (text_x + text_size[0] + 2, text_y + 2)
            cv2.rectangle(vis, bg_top_left, bg_bottom_right, color, -1)

            # 绘制文本（白色）
            cv2.putText(
                vis, text,
                (text_x, text_y - 2),
                cv2.FONT_HERSHEY_SIMPLEX,
                self.font_scale,
                (255, 255, 255),  # 白色文字
                thickness=1,
                lineType=cv2.LINE_AA  # 抗锯齿
            )

    @classmethod
    def create_custom_classes(cls, class_configs: List[dict]) -> Set[DetectionClass]:
        """
        工厂方法：创建自定义的类别集合

        Args:
            class_configs: 类别配置列表，每个元素包含 class_id, class_name, color
                示例: [
                    {"class_id": 2, "class_name": "轿车", "color": (0, 255, 0)},
                    {"class_id": 3, "class_name": "摩托车", "color": (255, 0, 0)}
                ]

        Returns:
            自定义的DetectionClass集合
        """
        custom_classes = set()
        for config in class_configs:
            try:
                detection_class = DetectionClass(
                    class_id=config["class_id"],
                    class_name=config["class_name"],
                    color=tuple(config["color"])
                )
                custom_classes.add(detection_class)
            except KeyError as e:
                raise ValueError(f"类别配置缺少必要字段: {e}")
            except Exception as e:
                raise ValueError(f"创建自定义类别失败: {e}")
        return custom_classes